Pafos
Model pick
50%
Fair odds 2.02
Midtjylland
26%
Fair odds 3.85
Share of the pitch
Who wins
| Pafos | Draw | Midtjylland | |
|---|---|---|---|
| Goals model | 50% | 24% | 26% |
| Elo model | 34% | 28% | 38% |
Why this prediction
-
Overall strength Favours Midtjylland
Midtjylland are rated 80 points above Pafos (1506 to 1426), built from every result, weighted towards recent ones.
-
Home advantage Favours Pafos
Playing at home is worth about 65 rating points, which adds 9 points to Pafos's chance of winning and takes the same from Midtjylland's.
-
Attack against defence Favours Pafos
Expected goals 1.8 to 1.2. Pafos score 1.1 a match against an average side; Midtjylland concede 1.7 (5th best defence in Danish Superliga).
-
Last 5 matches Favours Pafos
Pafos LDLWL, gained 14 rating points; Midtjylland LWWLW, lost 12 rating points. Already counted in the ratings above.
Rating, last 12 months
PafosMidtjylland
Pafos
5/10 over 2.5
11 scored
18 conceded
Midtjylland
7/10 over 2.5
19 scored
18 conceded
Bars show total goals in each of the last 10 matches, oldest on the left: the team's goals in its colour, conceded in grey. The dashed line is 2.5 goals.
Total goals
Both teams to score & clean sheets
Most likely scores
- 1-1
- 2-1
- 1-0
- 2-0
- 1-2
- 2-2
- 3-1
- 0-1
Every score
| 0 | 1 | 2 | 3 | 4 | 5 | |
|---|---|---|---|---|---|---|
| 0 | 6 | 6 | 4 | 2 | ||
| 1 | 8 | 11 | 7 | 3 | 1 | |
| 2 | 8 | 10 | 6 | 2 | 1 | |
| 3 | 5 | 6 | 3 | 1 | ||
| 4 | 2 | 2 | 2 | 1 | ||
| 5 | 1 | 1 | 1 |
Half-time result
First-half goals
Half-time / full-time
| Half-time ↓ Full-time → | Pafos | Draw | Midtjylland |
|---|---|---|---|
| Pafos | 30%3.36 | 4%22.99 | 1%70.42 |
| Level | 17%5.91 | 14%7.37 | 9%11.60 |
| Midtjylland | 3%29.76 | 6%17.73 | 16%6.11 |
Corners
Most corners
Corner totals are hard to call: over 9.5 the model's log loss is 0.685 against 0.693 for a naive guess, across 30,937 past matches. Which side wins more corners is called much better.
Cards
Over 4.5 cards, the model's log loss is 0.653 against 0.682 for a naive guess, across 33,146 past matches. The referee matters a lot and isn't in the model.
They haven't met in the data.